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Intelligence without Action is just a novelty.

Most AI pilots fail because they treat AI as a magic box. They get chatty, hallucinating toys.

From PoC to production • Custom solutions

Core services

We focus on one thing: integrating AI into your existing systems

AI Integration & Orchestration

Embed LLMs and agents into your existing tools and APIs (ERP/CRM/ITSM/warehouse systems).

  • • Event-driven flows, not cron spaghetti
  • • Idempotent, testable components
  • • Versioned prompts and policies
Kafka/SQSworker poolsfeature storesLangChain/LangGraphAzure OpenAI/OpenAIvector DBs

Data & Metadata Architecture

Make your data findable, trustworthy, and governed so AI can use it safely.

  • • Schemas, lineage, and entity modeling
  • • ETL/ELT pipelines with validation and contracts
  • • Access controls, redaction, and PII handling
PostgreSQL/BigQuerydbtAirflowOpenLineageDataHubLakehouse patterns

Retrieval-Augmented Generation (RAG) & Knowledge Search

Connect knowledge across docs, tickets, code, and databases.

  • • Fit-for-purpose chunking & embeddings
  • • Hybrid retrieval (semantic + keyword + metadata filters)
  • • Cited, auditable answers with confidence signals
OpenAI/AzureLlamaIndex/LangChainQdrant/Weaviate/PG-VectorElasticsearch

AI Agents for Operations

Task-specific agents that coordinate with your systems, not just chat.

  • • Scoped responsibilities, explicit tools, safe guards
  • • Human approval steps where required
  • • Full telemetry for actions and outcomes
Use cases: order opsinventory & supply updatescustomer opsfinance reconciliationIT runbooks

Cloud & MLOps for AI Workloads

Infrastructure that keeps latency low, costs sane, and security tight.

  • • Containerized services, autoscaling, secrets, and key management
  • • Prompt/config registries, feature stores, model gateways
  • • Observability: traces, metrics, red-teaming, drift detection
Azure/AWS/GCPKubernetesTerraform/TofuGrafana/PrometheusOpenTelemetry

How we build AI that actually works

Most AI vendors give you a chatbot. We build systems with three layers working together—so your AI understands context, makes smart decisions, and takes real action.

Context Layer

The Context Layer (Metadata)

AI that understands your business rules, not just your data. We structure everything with clear metadata—who created it, when it was updated, and what security level it has. This means your AI respects your policies and cites sources instead of guessing.

Reasoning Layer

The Reasoning Layer (Intelligence)

AI that thinks, not memorizes. We don't try to make AI memorize your entire business. Instead, we build systems that analyze your current context, understand what you need, and create a plan. You can swap models as better ones emerge without rebuilding.

Integration Layer

The Action Layer (Integration)

AI that executes actions in your systems, not just suggestions. Your AI safely executes actions in your ERP, CRM, and other systems. With human approval gates where needed, full audit trails, and the ability to roll back if something goes wrong.

Reliable AI for Complex Environments

We bring Generative AI out of the sandbox and into your core architecture. Laava focuses on deep integration: embedding intelligent agents directly into your existing stack to automate complex workflows and modernize legacy systems.

We don't build isolated chatbots; we engineer resilient, scalable AI infrastructure that executes work rather than just talking about it. No throwaway POCs, just secure, auditable impact.

Selected work

Hyper-Personalized Financial Coaching

Challenge

AI financial coach that analyzes spending, sets smart budgets and gives proactive savings tips, so customers build healthier habits without doing complex calculations themselves.

Solution

Hyper-Personalized Financial Coaching turns transaction data into simple budgets, alerts and suggestions that fit each person’s situation. The AI coach explains changes in plain language, proposes small concrete actions and adapts over time, so customers save more each month, feel in control and build a healthier financial habit inside the bank’s own app.

The 'Smart KYC' Analyst

Challenge

AI KYC analyst that automatically checks public sources and sanction lists and creates a clear risk profile for new clients, so onboarding is faster and more secure.

Solution

The Smart KYC Analyst is an autonomous agent that gathers data from all agreed sources, interprets it using your risk framework and drafts a clear, structured risk profile for each client. Human analysts stay in control of the final decision, but they review one concise summary instead of dozens of raw sources, which speeds up onboarding, reduces manual work and makes risk decisions more consistent.

The CSRD Reporting Agent

Challenge

GenAI automatically generates narrative paragraphs for sustainability reports based on raw internal data and legal texts.

Solution

The CSRD Reporting Agent connects to your existing ESG dashboards and data sources, reads the CSRD requirements and drafts consistent narrative sections for each topic. It explains trends, links claims to underlying numbers and keeps wording aligned across business units, so your team focuses on review and governance instead of manual drafting.

Computer Vision Quality Inspection

Challenge

High-resolution cameras inspect every product on the line for tiny defects so issues are caught before shipping, without slowing down production.

Solution

The Computer Vision Quality Inspection system uses high-resolution cameras and AI models to inspect every product in real time. It flags tiny defects, supports operators with clear images and dashboards, and helps you reach higher detection rates with fewer manual checks and more stable quality.

FAQ

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Ready to integrate AI into your operations?

Let's map your systems and identify the highest-ROI flows.

We'll review your architecture, data, and constraints, then propose a focused pilot with timeline and budget.